neon-aqu-aos.mysite = 'PRLA'
See how many locations chl data is collected from
See how many locations chl data is collected from (variable Y axis)
Availability of chlorophyll AOS data for each flight date
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | NA | 2019-07-11 | NA | 1107 | 1107 | FALSE | FALSE |
| 2017-06-21 | NA | 2019-07-11 | NA | 750 | 750 | FALSE | FALSE |
| 2017-06-26 | NA | 2019-07-11 | NA | 745 | 745 | FALSE | FALSE |
| 2019-07-26 | 2019-07-11 | 2019-09-10 | 15 | 46 | 15 | TRUE | TRUE |
| 2019-07-27 | 2019-07-11 | 2019-09-10 | 16 | 45 | 16 | TRUE | TRUE |
| 2019-07-30 | 2019-07-11 | 2019-09-10 | 19 | 42 | 19 | TRUE | TRUE |
| 2020-06-24 | 2020-06-15 | 2020-09-29 | 9 | 97 | 9 | TRUE | TRUE |
| 2020-06-26 | 2020-06-15 | 2020-09-29 | 11 | 95 | 11 | TRUE | TRUE |
| 2020-07-02 | 2020-06-15 | 2020-09-29 | 17 | 89 | 17 | TRUE | TRUE |
| flightdate | aos_match | days |
|---|---|---|
| 2019-07-26 | 2019-07-11 | 15 |
| 2019-07-27 | 2019-07-11 | 16 |
| 2019-07-30 | 2019-07-11 | 19 |
| 2020-06-24 | 2020-06-15 | 9 |
| 2020-06-26 | 2020-06-15 | 11 |
| 2020-07-02 | 2020-06-15 | 17 |
## [1] "microgramsPerLiter"
## [1] "condition ok"
These are the closest chl AOS values for each flight date
| aos_match | flightdate | days | pheophytin | chlorophyll a |
|---|---|---|---|---|
| 2019-07-11 | 2019-07-26 | 15 | 3.01 | 2.31 |
| 2019-07-11 | 2019-07-27 | 16 | 3.01 | 2.31 |
| 2019-07-11 | 2019-07-30 | 19 | 3.01 | 2.31 |
| 2020-06-15 | 2020-06-24 | 9 | 4.20 | 3.45 |
| 2020-06-15 | 2020-06-26 | 11 | 4.20 | 3.45 |
| 2020-06-15 | 2020-07-02 | 17 | 4.20 | 3.45 |
Algal types include:
Algal divisions include:
Algal classes include:
Abundance is reported in a range of units:
Starting in 2018 algal type no longer reported?
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## namedLocation = col_character(),
## collectDate = col_date(format = ""),
## sampleID = col_character(),
## algalParameter = col_character(),
## class = col_character(),
## sum_param = col_double(),
## rel_abun = col_double()
## )
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## namedLocation = col_character(),
## collectDate = col_date(format = ""),
## sampleID = col_character(),
## algalParameter = col_character(),
## division = col_character(),
## sum_param = col_double(),
## rel_abun = col_double()
## )
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## namedLocation = col_character(),
## collectDate = col_date(format = ""),
## sampleID = col_character(),
## algalParameter = col_character(),
## algalType = col_character(),
## sum_param = col_double(),
## rel_abun = col_double()
## )
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
The EXO total algae sensor is a dual‐channel fluorometer that uses a 470nm excitation beam that excites chlorophyll a and a second 590 nm excitation beam that excites the phyocyanin accessory pigment found in blue‐green algae (cyanobacteria). Chlorophyll concentration is a biogeochemically relavant parameter that is readily available by remote sensing and can be can serve as a proxy for phytoplankton biomass and light attenuation (Oestreich et al., 2016, Ganju et al., 2014, Jaud et al., 2012)
Sensor chl data availability
| flightline_datetime | check_30day | check_10day | check_1day | check_12hr |
|---|---|---|---|---|
| 2016-06-29 15:45:47 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 15:52:09 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 17:22:24 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-21 20:58:28 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 15:33:21 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:04:51 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:11:11 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-26 15:20:23 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 16:22:11 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 22:09:25 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-27 15:27:04 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-30 15:34:20 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 17:36:27 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:26:15 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:18:44 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-26 19:28:06 | TRUE | TRUE | TRUE | FALSE |
| 2020-07-02 17:14:10 | TRUE | TRUE | FALSE | FALSE |
zoo::rollapply.tsibbleGet the moving average value closet to flight time.
| collectDateTime | datetime | chl5min | chl_ma01 | chl_ma03 | chl_ma04 | chl_ma04u | chl_ma06 | chl_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2019-07-26 15:20:23 | 2019-07-26 15:20:00 | 5.83 | 6.245 | 6.285 | 6.290 | 6.312174 | 6.350 | 6.610 |
| 2019-07-26 16:22:11 | 2019-07-26 16:20:00 | 6.70 | 6.175 | 6.285 | 6.310 | 6.327500 | 6.425 | 6.610 |
| 2019-07-26 22:09:25 | 2019-07-26 22:10:00 | 7.47 | 7.665 | 7.735 | 7.695 | 7.881042 | 7.660 | 7.650 |
| 2019-07-27 15:27:04 | 2019-07-27 15:25:00 | 8.16 | 8.195 | 8.380 | 8.400 | 8.477083 | 8.455 | 8.655 |
| 2019-07-30 15:34:20 | 2019-07-30 15:35:00 | 18.12 | 17.850 | 17.460 | 17.460 | 17.539130 | 17.860 | 19.670 |
zoo::rollapply.tsibbleGet the moving average value closet to flight time
| collectDateTime | datetime | chl5min | chl_ma01 | chl_ma03 | chl_ma04 | chl_ma04u | chl_ma06 | chl_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2020-06-24 17:36:27 | 2020-06-24 17:35:00 | 2.54 | 2.320 | 2.48 | 2.545 | 2.638333 | 2.715 | 3.150 |
| 2020-06-24 16:26:15 | 2020-06-24 16:25:00 | 2.74 | 2.565 | 2.56 | 2.565 | 2.581875 | 2.680 | 3.165 |
| 2020-06-24 16:18:44 | 2020-06-24 16:20:00 | 2.54 | 2.565 | 2.56 | 2.585 | 2.587917 | 2.680 | 3.175 |
| 2020-06-26 19:28:06 | 2020-06-26 19:30:00 | NA | NA | NA | NA | NaN | NA | NA |
| 2020-07-02 17:14:10 | 2020-07-02 17:15:00 | NA | NA | NA | NA | NaN | NA | NA |
chla_df date and times with the aop/ais matching function to determine which sampling dates have sensor data from same day around time of sampling.already sort of done for suna data… follow this pattern
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2019-07-11 13:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-10 13:37:00 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-15 15:09:00 | TRUE | TRUE | TRUE | TRUE |
| 2020-09-29 13:21:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-04-20 14:40:00 | FALSE | FALSE | FALSE | FALSE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2019-07-11 13:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-10 13:37:00 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-15 15:09:00 | TRUE | TRUE | TRUE | TRUE |
| collect_date | pheophytin | chla | chl_tot |
|---|---|---|---|
| 2019-07-11 | 3.01 | 2.31 | 5.32 |
| 2019-09-10 | 3.08 | 2.79 | 5.87 |
| 2020-06-15 | 4.20 | 3.45 | 7.65 |
| 2020-09-29 | 72.40 | 124.44 | 196.84 |
| 2021-04-20 | 21.38 | 20.03 | 41.41 |
Compare AOS and AIS
EPA Method description:
The UVA procedure requires that the sample be passed through a 0.45 um filter and transferred to quartz cell. It is then placed in a spectrophotometer to measure the UV absorbance at 254 nm and reported in cm -1.
The SUVA procedure requires both the DOC and UVA measurement. The SUVA is then calculated by dividing the UV absorbance of the sample (in cm -1) by the DOC of the sample (in mg/L) and then multiplying by 100 cm/M. SUVA is reported in units of L/mg-M. The formula for the SUVA may be found in Section 12.2.
Ignoring data before 2016
## Warning: Removed 2 rows containing missing values (geom_point).
Correct older UV Absorbance wavelengths from 250 to 254, assuming all 250 nm should be 254.
swchem_site_df <- swchem_site_df %>%
dplyr::mutate(analyte = dplyr::case_when(analyte == 'UV Absorbance (250 nm)' ~
'UV Absorbance (254 nm)', TRUE ~ analyte))
## Warning: Removed 2 rows containing missing values (geom_point).
How much does SUVA 254/280 ratio change?
How much does organic carbon dissolved proportion change?
Adjusts sample IDs in a new column to group together raw and filtered, while keeping track of duplicates
## Warning: Removed 11 rows containing missing values (geom_point).
Match DOM AOS and AOP data
Need to do this separately for each analyte because they arent always all reported.
These are the closest DOC values (mg/L) for each flight date
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | 2014-06-19 | 2016-10-19 | 741 | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2020-06-09 | 2020-08-04 | 15 | 41 | 15 | TRUE | TRUE |
| 2020-06-26 | 2020-06-09 | 2020-08-04 | 17 | 39 | 17 | TRUE | TRUE |
| 2020-07-02 | 2020-06-09 | 2020-08-04 | 23 | 33 | 23 | TRUE | TRUE |
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
| 2020-06-24 | 2020-06-09 | 15 |
| 2020-06-26 | 2020-06-09 | 17 |
| 2020-07-02 | 2020-06-09 | 23 |
| aos_match | flightdate | days | DOC |
|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 29.97 |
| 2017-07-06 | 2017-06-26 | 10 | 31.68, 31.81, 32.11 |
| 2019-08-06 | 2019-07-26 | 11 | 35.01 |
| 2019-08-06 | 2019-07-27 | 10 | 35.01 |
| 2019-08-06 | 2019-07-30 | 7 | 35.01 |
| 2020-06-09 | 2020-06-24 | 15 | 26.72 |
| 2020-06-09 | 2020-06-26 | 17 | 26.72 |
| 2020-06-09 | 2020-07-02 | 23 | 26.72 |
These are the closest UV abs values (per 1 cm) for each flight date
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | NA | 2016-10-19 | NA | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2019-12-04 | 2020-08-04 | 203 | 41 | 41 | FALSE | TRUE |
| 2020-06-26 | 2019-12-04 | 2020-08-04 | 205 | 39 | 39 | FALSE | TRUE |
| 2020-07-02 | 2019-12-04 | 2020-08-04 | 211 | 33 | 33 | FALSE | TRUE |
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
| 2020-06-24 | 2020-08-04 | 41 |
| 2020-06-26 | 2020-08-04 | 39 |
| 2020-07-02 | 2020-08-04 | 33 |
| aos_match | flightdate | days | UV Absorbance (254 nm) |
|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 0.5578 |
| 2017-07-06 | 2017-06-26 | 10 | 0.5566, 0.5710, 0.5653 |
| 2019-08-06 | 2019-07-26 | 11 | 0.7538 |
| 2019-08-06 | 2019-07-27 | 10 | 0.7538 |
| 2019-08-06 | 2019-07-30 | 7 | 0.7538 |
| 2020-08-04 | 2020-06-24 | 41 | 0.5646 |
| 2020-08-04 | 2020-06-26 | 39 | 0.5646 |
| 2020-08-04 | 2020-07-02 | 33 | 0.5646 |
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | NA | 2016-10-19 | NA | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2019-12-04 | 2020-08-04 | 203 | 41 | 41 | FALSE | TRUE |
| 2020-06-26 | 2019-12-04 | 2020-08-04 | 205 | 39 | 39 | FALSE | TRUE |
| 2020-07-02 | 2019-12-04 | 2020-08-04 | 211 | 33 | 33 | FALSE | TRUE |
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
| 2020-06-24 | 2020-08-04 | 41 |
| 2020-06-26 | 2020-08-04 | 39 |
| 2020-07-02 | 2020-08-04 | 33 |
| aos_match | flightdate | days | UV Absorbance (280 nm) |
|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 0.2876 |
| 2017-07-06 | 2017-06-26 | 10 | 0.2887, 0.2961, 0.2950 |
| 2019-08-06 | 2019-07-26 | 11 | 0.3852 |
| 2019-08-06 | 2019-07-27 | 10 | 0.3852 |
| 2019-08-06 | 2019-07-30 | 7 | 0.3852 |
| 2020-08-04 | 2020-06-24 | 41 | 0.3118 |
| 2020-08-04 | 2020-06-26 | 39 | 0.3118 |
| 2020-08-04 | 2020-07-02 | 33 | 0.3118 |
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | 2014-06-19 | 2016-10-19 | 741 | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2020-06-09 | 2020-06-30 | 15 | 6 | 6 | TRUE | TRUE |
| 2020-06-26 | 2020-06-09 | 2020-06-30 | 17 | 4 | 4 | TRUE | TRUE |
| 2020-07-02 | 2020-06-30 | 2020-08-04 | 2 | 33 | 2 | TRUE | TRUE |
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
| 2020-06-24 | 2020-06-30 | 6 |
| 2020-06-26 | 2020-06-30 | 4 |
| 2020-07-02 | 2020-06-30 | 2 |
| aos_match | flightdate | days | TOC |
|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 34.12 |
| 2017-07-06 | 2017-06-26 | 10 | 37.87, 38.11, 38.82 |
| 2019-08-06 | 2019-07-26 | 11 | 36.05 |
| 2019-08-06 | 2019-07-27 | 10 | 36.05 |
| 2019-08-06 | 2019-07-30 | 7 | 36.05 |
| 2020-06-30 | 2020-06-24 | 6 | 26.05, 26.49, 26.18 |
| 2020-06-30 | 2020-06-26 | 4 | 26.05, 26.49, 26.18 |
| 2020-06-30 | 2020-07-02 | 2 | 26.05, 26.49, 26.18 |
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | 2014-06-19 | 2016-10-19 | 741 | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2019-12-04 | 2021-05-04 | 203 | 314 | 203 | FALSE | FALSE |
| 2020-06-26 | 2019-12-04 | 2021-05-04 | 205 | 312 | 205 | FALSE | FALSE |
| 2020-07-02 | 2019-12-04 | 2021-05-04 | 211 | 306 | 211 | FALSE | FALSE |
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
| aos_match | flightdate | days | Fe |
|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 5e-04 |
| 2017-07-06 | 2017-06-26 | 10 | 5e-04, 5e-04, 5e-04 |
| 2019-08-06 | 2019-07-26 | 11 | 0.01 |
| 2019-08-06 | 2019-07-27 | 10 | 0.01 |
| 2019-08-06 | 2019-07-30 | 7 | 0.01 |
The EXO fDOM sensor is a fluorometer with a single excitation/emission pair (365nm/480nm) used to detect the fluorescent fraction of the chromophoric DOM when exposed to near‐UV light. Because of the impacts of temperature and water column absorbance (from a combination of dissolved and particulate compounds) on these readings corrections must be applied to the calibrated data.
| flightline_datetime | check_30day | check_10day | check_1day | check_12hr |
|---|---|---|---|---|
| 2016-06-29 15:45:47 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 15:52:09 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 17:22:24 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-21 20:58:28 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 15:33:21 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:04:51 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:11:11 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-26 15:20:23 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 16:22:11 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 22:09:25 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-27 15:27:04 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-30 15:34:20 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 17:36:27 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:26:15 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:18:44 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-26 19:28:06 | TRUE | TRUE | TRUE | FALSE |
| 2020-07-02 17:14:10 | TRUE | TRUE | FALSE | FALSE |
## Warning: Removed 3170 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 8658 rows containing missing values (geom_point).
## Warning: Removed 2552 rows containing missing values (geom_point).
## Warning: Removed 444 row(s) containing missing values (geom_path).
## Warning: Removed 444 rows containing missing values (geom_point).
## Warning: Removed 4737 rows containing missing values (geom_point).
zoo::rollapply.tsibbleGet the moving average value closet to flight time.
| collectDateTime | datetime | fdom5min | ma01 | ma03 | ma04 | ma04u | ma06 | ma12 |
|---|---|---|---|---|---|---|---|---|
| 2019-07-26 15:20:23 | 2019-07-26 15:20:00 | 109.76 | 109.620 | 109.620 | 109.630 | 109.6151 | 109.630 | 109.63 |
| 2019-07-26 16:22:11 | 2019-07-26 16:20:00 | 109.22 | 109.260 | 109.260 | 109.260 | 109.2767 | 109.260 | 109.26 |
| 2019-07-26 22:09:25 | 2019-07-26 22:10:00 | 108.23 | 108.075 | 108.065 | 108.065 | 108.0837 | 108.070 | 108.09 |
| 2019-07-27 15:27:04 | 2019-07-27 15:25:00 | 105.65 | 105.760 | 105.745 | 105.745 | 105.7490 | 105.745 | 105.80 |
| 2019-07-30 15:34:20 | 2019-07-30 15:35:00 | 100.23 | 100.380 | 100.350 | 100.310 | 100.2890 | 100.340 | 100.35 |
zoo::rollapply.tsibbleGet the moving average value closet to flight time.
| collectDateTime | datetime | fdom5min | ma01 | ma03 | ma04 | ma04u | ma06 | ma12 |
|---|---|---|---|---|---|---|---|---|
| 2020-06-24 17:36:27 | 2020-06-24 17:35:00 | 69.59 | 69.625 | 69.700 | 69.730 | 69.53479 | 69.865 | 70.200 |
| 2020-06-24 16:26:15 | 2020-06-24 16:25:00 | 70.02 | 69.855 | 69.880 | 69.880 | 69.94437 | 69.850 | 70.105 |
| 2020-06-24 16:18:44 | 2020-06-24 16:20:00 | 69.73 | 69.945 | 69.955 | 69.955 | 69.96188 | 69.850 | 70.105 |
| 2020-06-26 19:28:06 | 2020-06-26 19:30:00 | NA | NA | NA | NA | NaN | NA | NA |
| 2020-07-02 17:14:10 | 2020-07-02 17:15:00 | NA | NA | NA | NA | NaN | NA | NA |
use swchem_df date and times with the aop/ais matching function to determine which sampling dates have sensor data from same day around time of sampling.
Get fDOM sensor data from all AOS sampling dates (use ais with buffer2 function?)
show what is available
calculate rolling averages of fDOM time series
get rolling average values for time closest to sampling using which min on difftime
little overlap because of seasonal removal of buoys
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2014-05-14 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2014-06-19 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-10-19 15:30:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-11-16 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-12-19 17:53:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-01-18 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-02-13 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-03-22 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-04-12 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-05-03 14:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-08 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-07-06 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-08-03 14:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-09-06 14:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-10-19 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-11-01 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-01-04 17:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-02-13 16:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-03-13 16:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-04-17 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-05-01 16:26:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-06-05 15:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-30 16:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-12-04 16:42:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-01-10 17:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-02-11 17:55:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-03-26 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-05-07 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-12-04 17:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-01-09 15:12:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-02-11 15:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-03-10 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-06-09 15:35:00 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-30 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-08-04 15:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-09-02 13:27:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-01-07 16:08:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-02-02 17:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-03-02 18:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-04-07 15:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-09 15:35:00 | TRUE | TRUE | TRUE | TRUE |
| collect_date | Fe | TOC | DOC | abs254 | abs280 |
|---|---|---|---|---|---|
| 2018-07-05 | 0.0005 | 42.81 | 34.46 | 0.6168 | 0.3113 |
| 2018-08-07 | 0.0005 | 47.99 | 37.81 | 0.6288 | 0.3172 |
| 2018-09-04 | 0.0005 | 41.88 | 39.81 | 0.6569 | 0.3274 |
| 2018-10-16 | 0.0005 | 42.74 | 39.67 | 0.6455 | 0.3171 |
| 2019-06-04 | 0.0005 | 38.82 | 35.19 | 0.5498 | 0.2846 |
| 2019-07-01 | 0.0042 | 36.23 | 35.90 | 0.7863 | 0.4191 |
| 2019-07-01 | 0.0048 | 34.88 | 35.91 | 0.7881 | 0.4111 |
| 2019-07-01 | 0.0040 | 36.78 | 35.79 | 0.7881 | 0.4048 |
| 2019-08-06 | 0.0100 | 36.05 | 35.01 | 0.7538 | 0.3852 |
| 2019-09-04 | 0.0025 | 33.08 | 32.87 | 0.6865 | 0.3524 |
| 2019-09-04 | 0.0030 | 32.76 | 33.03 | 0.6967 | 0.3607 |
| 2019-09-04 | 0.0026 | 32.82 | 33.16 | 0.6503 | 0.3363 |
| 2019-10-16 | 0.0005 | 27.63 | 27.56 | 0.6416 | 0.3293 |
| 2020-06-09 | NA | 26.65 | 26.72 | NA | NA |
Same plot as above with only QF = 0 data
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2014-05-14 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2014-06-19 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-10-19 15:30:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-11-16 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-12-19 17:53:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-01-18 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-02-13 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-03-22 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-04-12 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-05-03 14:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-08 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-07-06 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-08-03 14:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-09-06 14:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-10-19 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-11-01 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-01-04 17:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-02-13 16:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-03-13 16:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-04-17 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-05-01 16:26:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-06-05 15:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-07-05 15:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-08-07 16:16:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-09-04 15:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-10-16 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-10-30 16:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-12-04 16:42:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-01-10 17:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-02-11 17:55:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-03-26 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-05-07 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-06-04 15:25:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-12-04 17:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-01-09 15:12:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-02-11 15:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-03-10 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-06-09 15:35:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-06-30 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-08-04 15:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-09-02 13:27:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-01-07 16:08:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-02-02 17:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-03-02 18:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-04-07 15:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
## Warning: Removed 641 rows containing missing values (geom_point).
Compare AOS and AIS
Colors indicate month
| flightline_datetime | check_30day | check_10day | check_1day | check_12hr |
|---|---|---|---|---|
| 2016-06-29 15:45:47 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 15:52:09 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 17:22:24 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-21 20:58:28 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 15:33:21 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:04:51 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:11:11 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-26 15:20:23 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 16:22:11 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 22:09:25 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-27 15:27:04 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-30 15:34:20 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 17:36:27 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:26:15 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:18:44 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-26 19:28:06 | TRUE | TRUE | TRUE | TRUE |
| 2020-07-02 17:14:10 | TRUE | TRUE | TRUE | TRUE |
| flightline_datetime | check_30day | check_10day | check_1day | check_12hr |
|---|---|---|---|---|
| 2019-07-26 15:20:23 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 16:22:11 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 22:09:25 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-27 15:27:04 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-30 15:34:20 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 17:36:27 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:26:15 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:18:44 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-26 19:28:06 | TRUE | TRUE | TRUE | TRUE |
| 2020-07-02 17:14:10 | TRUE | TRUE | TRUE | TRUE |
| flightline_datetime | check_1day | interval3_start | interval3_end |
|---|---|---|---|
| 2019-07-26 15:20:23 | TRUE | 2019-07-23 | 2019-07-29 |
| 2019-07-26 16:22:11 | TRUE | 2019-07-23 | 2019-07-29 |
| 2019-07-26 22:09:25 | TRUE | 2019-07-23 | 2019-07-29 |
| 2019-07-27 15:27:04 | TRUE | 2019-07-24 | 2019-07-30 |
| 2019-07-30 15:34:20 | TRUE | 2019-07-27 | 2019-08-02 |
| 2020-06-24 17:36:27 | TRUE | 2020-06-21 | 2020-06-27 |
| 2020-06-24 16:26:15 | TRUE | 2020-06-21 | 2020-06-27 |
| 2020-06-24 16:18:44 | TRUE | 2020-06-21 | 2020-06-27 |
| 2020-06-26 19:28:06 | TRUE | 2020-06-23 | 2020-06-29 |
| 2020-07-02 17:14:10 | TRUE | 2020-06-29 | 2020-07-05 |
NEON-L0-SUNA/AOP-dates/{mysite}/{mysite}-103-2020.csvprocess-L0-SUNA in processing project## Rows: 803
## Columns: 9
## Delimiter: ","
## dbl [8]: mean_abs254, sd_abs254, n_abs254, se_abs254, mean_abs350, sd_abs350, n_abs350,...
## dttm [1]: burst_id
##
## Use `spec()` to retrieve the guessed column specification
## Pass a specification to the `col_types` argument to quiet this message
| collectDateTime | datetime | mean_abs254 | abs254_ma01 | abs254_ma03 | abs254_ma04 | abs254_ma04u | abs254_ma06 | abs254_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2019-07-26 15:20:23 | 2019-07-26 15:15:00 | 0.7240 | 0.7223 | 0.7223 | 0.7224 | 0.7246 | 0.7224 | 0.7220 |
| 2019-07-26 16:22:11 | 2019-07-26 16:15:00 | 0.7213 | 0.7213 | 0.7220 | 0.7220 | 0.7235 | 0.7220 | 0.7218 |
| 2019-07-26 22:09:25 | 2019-07-26 22:15:00 | 0.7213 | 0.7201 | 0.7182 | 0.7182 | 0.7175 | 0.7182 | 0.7182 |
| 2019-07-27 15:27:04 | 2019-07-27 15:30:00 | 0.6946 | 0.6944 | 0.6928 | 0.6925 | 0.6904 | 0.6928 | 0.6931 |
| 2019-07-30 15:34:20 | 2019-07-30 15:30:00 | 0.6522 | 0.6524 | 0.6523 | 0.6531 | 0.6536 | 0.6560 | 0.6577 |
| collectDateTime | datetime | mean_abs350 | abs350_ma01 | abs350_ma03 | abs350_ma04 | abs350_ma04u | abs350_ma06 | abs350_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2019-07-26 15:20:23 | 2019-07-26 15:15:00 | 0.1961 | 0.1945 | 0.1945 | 0.1945 | 0.1966 | 0.1945 | 0.1938 |
| 2019-07-26 16:22:11 | 2019-07-26 16:15:00 | 0.1936 | 0.1936 | 0.1936 | 0.1936 | 0.1954 | 0.1936 | 0.1931 |
| 2019-07-26 22:09:25 | 2019-07-26 22:15:00 | 0.1912 | 0.1905 | 0.1903 | 0.1903 | 0.1887 | 0.1903 | 0.1902 |
| 2019-07-27 15:27:04 | 2019-07-27 15:30:00 | 0.1660 | 0.1659 | 0.1654 | 0.1653 | 0.1636 | 0.1654 | 0.1655 |
| 2019-07-30 15:34:20 | 2019-07-30 15:30:00 | 0.1245 | 0.1245 | 0.1243 | 0.1258 | 0.1269 | 0.1278 | 0.1315 |
## Rows: 755
## Columns: 9
## Delimiter: ","
## dbl [8]: mean_abs254, sd_abs254, n_abs254, se_abs254, mean_abs350, sd_abs350, n_abs350,...
## dttm [1]: burst_id
##
## Use `spec()` to retrieve the guessed column specification
## Pass a specification to the `col_types` argument to quiet this message
| collectDateTime | datetime | mean_abs254 | abs254_ma01 | abs254_ma03 | abs254_ma04 | abs254_ma04u | abs254_ma06 | abs254_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2020-06-24 17:36:27 | 2020-06-24 17:30:00 | 0.4602 | 0.4606 | 0.4606 | 0.4607 | 0.4609 | 0.4609 | 0.4611 |
| 2020-06-24 16:26:15 | 2020-06-24 16:30:00 | 0.4600 | 0.4600 | 0.4602 | 0.4604 | 0.4608 | 0.4609 | 0.4611 |
| 2020-06-24 16:18:44 | 2020-06-24 16:15:00 | 0.4598 | 0.4600 | 0.4602 | 0.4604 | 0.4609 | 0.4608 | 0.4612 |
| 2020-06-26 19:28:06 | 2020-06-26 19:30:00 | 0.4726 | 0.4727 | 0.4727 | 0.4727 | 0.4733 | 0.4727 | 0.4732 |
| collectDateTime | datetime | mean_abs350 | abs350_ma01 | abs350_ma03 | abs350_ma04 | abs350_ma04u | abs350_ma06 | abs350_ma12 |
|---|---|---|---|---|---|---|---|---|
| 2020-06-24 17:36:27 | 2020-06-24 17:30:00 | 0.0449 | 0.0450 | 0.0452 | 0.0452 | 0.0455 | 0.0457 | 0.0456 |
| 2020-06-24 16:26:15 | 2020-06-24 16:30:00 | 0.0449 | 0.0447 | 0.0448 | 0.0450 | 0.0453 | 0.0455 | 0.0457 |
| 2020-06-24 16:18:44 | 2020-06-24 16:15:00 | 0.0443 | 0.0447 | 0.0447 | 0.0450 | 0.0453 | 0.0454 | 0.0458 |
| 2020-06-26 19:28:06 | 2020-06-26 19:30:00 | 0.0545 | 0.0548 | 0.0548 | 0.0548 | 0.0550 | 0.0548 | 0.0554 |
This is the availability of SUNA data for AOS sampling dates
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2016-10-19 15:30:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-11-16 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-12-19 17:53:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-01-18 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-02-13 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-03-22 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-04-12 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-05-03 14:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-08 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-07-06 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-08-03 14:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-09-06 14:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-10-19 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-11-01 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-01-04 17:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-02-13 16:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-03-13 16:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-04-17 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-05-01 16:26:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-06-05 15:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-07-05 15:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-08-07 16:16:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-09-04 15:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-10-16 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-10-30 16:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-12-04 16:42:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-01-10 17:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-02-11 17:55:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-03-26 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-05-07 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-06-04 15:25:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-12-04 17:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-08-04 15:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2020-09-02 13:27:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-01-07 16:08:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-02-02 17:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-03-02 18:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-04-07 15:45:00 | TRUE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | TRUE | TRUE | TRUE | TRUE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2021-04-07 15:45:00 | TRUE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | TRUE | TRUE | TRUE | TRUE |
Helper table for manually pulling SUNA data (AOS dates):
| aos_datetime | check_1day | interval3_start | interval3_end |
|---|---|---|---|
| 2019-07-01 14:53:00 | TRUE | 2019-06-28 | 2019-07-04 |
| 2019-08-06 15:55:00 | TRUE | 2019-08-03 | 2019-08-09 |
| 2019-09-04 16:00:00 | TRUE | 2019-09-01 | 2019-09-07 |
| 2019-10-16 17:05:00 | TRUE | 2019-10-13 | 2019-10-19 |
| 2021-04-07 15:45:00 | FALSE | 2021-04-04 | 2021-04-10 |
| 2021-05-04 15:22:00 | TRUE | 2021-05-01 | 2021-05-07 |
NEON-processed/suna-L0-timeseriesRegression for sensor data within the same day
## `geom_smooth()` using formula 'y ~ x'
Data from all locations at site
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
sm_site_df <- sm_site_df %>%
dplyr::filter(namedLocation %in% my_loc)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
How much does TDS/TSS change?
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_point).
Match sediment aos to aop dates
source('R/match-aop-aos.R')
sm_match_list <- match_aop_aos(mysite_dates, sm_site_df, 'collect_date')
knitr::kable(sm_match_list$dates)
| flightdate | aos_before | aos_after | days_before | days_after | min_days | meets_thresh1 | meets_thresh2 |
|---|---|---|---|---|---|---|---|
| 2016-06-29 | 2014-06-19 | 2016-10-19 | 741 | 112 | 112 | FALSE | FALSE |
| 2017-06-21 | 2017-06-08 | 2017-07-06 | 13 | 15 | 13 | TRUE | TRUE |
| 2017-06-26 | 2017-06-08 | 2017-07-06 | 18 | 10 | 10 | TRUE | TRUE |
| 2019-07-26 | 2019-07-01 | 2019-08-06 | 25 | 11 | 11 | TRUE | TRUE |
| 2019-07-27 | 2019-07-01 | 2019-08-06 | 26 | 10 | 10 | TRUE | TRUE |
| 2019-07-30 | 2019-07-01 | 2019-08-06 | 29 | 7 | 7 | TRUE | TRUE |
| 2020-06-24 | 2019-12-04 | 2021-05-04 | 203 | 314 | 203 | FALSE | FALSE |
| 2020-06-26 | 2019-12-04 | 2021-05-04 | 205 | 312 | 205 | FALSE | FALSE |
| 2020-07-02 | 2019-12-04 | 2021-05-04 | 211 | 306 | 211 | FALSE | FALSE |
knitr::kable(sm_match_list$matches)
| flightdate | aos_match | days |
|---|---|---|
| 2017-06-21 | 2017-06-08 | 13 |
| 2017-06-26 | 2017-07-06 | 10 |
| 2019-07-26 | 2019-08-06 | 11 |
| 2019-07-27 | 2019-08-06 | 10 |
| 2019-07-30 | 2019-08-06 | 7 |
These are the closest in time ground-based measurements of TDS (mg/L) and TSS (mg/L)
| aos_match | flightdate | days | TDS | TSS | TSS - Dry Mass |
|---|---|---|---|---|---|
| 2017-06-08 | 2017-06-21 | 13 | 910.1 | 32 | 800 |
| 2017-07-06 | 2017-06-26 | 10 | 885.62, 887.64, 891.23 | 10, 9, 9 | 900, 900, 1000 |
| 2019-08-06 | 2019-07-26 | 11 | 744.6 | 10 | 500 |
| 2019-08-06 | 2019-07-27 | 10 | 744.6 | 10 | 500 |
| 2019-08-06 | 2019-07-30 | 7 | 744.6 | 10 | 500 |
The EXO turbidity sensor employs a near‐IR light source (~780 ‐ 900 nm) and detects scattering at 90 degrees of the incident beam.
| flightline_datetime | check_30day | check_10day | check_1day | check_12hr |
|---|---|---|---|---|
| 2016-06-29 15:45:47 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 15:52:09 | FALSE | FALSE | FALSE | FALSE |
| 2016-06-29 17:22:24 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-21 20:58:28 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 15:33:21 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:04:51 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-26 16:11:11 | FALSE | FALSE | FALSE | FALSE |
| 2019-07-26 15:20:23 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 16:22:11 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-26 22:09:25 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-27 15:27:04 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-30 15:34:20 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 17:36:27 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:26:15 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-24 16:18:44 | TRUE | TRUE | TRUE | TRUE |
| 2020-06-26 19:28:06 | TRUE | TRUE | TRUE | FALSE |
| 2020-07-02 17:14:10 | TRUE | TRUE | FALSE | FALSE |
## Warning: Removed 8537 row(s) containing missing values (geom_path).
## Warning: Removed 8658 rows containing missing values (geom_point).
## Warning: Removed 2438 row(s) containing missing values (geom_path).
## Warning: Removed 2552 rows containing missing values (geom_point).
## Warning: Removed 444 row(s) containing missing values (geom_path).
## Warning: Removed 444 rows containing missing values (geom_point).
## Warning: Removed 4737 row(s) containing missing values (geom_path).
## Warning: Removed 4737 rows containing missing values (geom_point).
zoo::rollapply.tsibbleGet the moving average value closet to flight time.
| collectDateTime | datetime | turb5min | ma01 | ma03 | ma04 | ma04u | ma06 | ma12 |
|---|---|---|---|---|---|---|---|---|
| 2019-07-26 15:20:23 | 2019-07-26 15:20:00 | 2.09 | 2.170 | 2.170 | 2.175 | 2.228913 | 2.180 | 2.240 |
| 2019-07-26 16:22:11 | 2019-07-26 16:20:00 | 2.17 | 2.205 | 2.230 | 2.240 | 2.326667 | 2.245 | 2.290 |
| 2019-07-26 22:09:25 | 2019-07-26 22:10:00 | 3.98 | 3.185 | 3.140 | 3.125 | 3.129167 | 3.080 | 2.925 |
| 2019-07-27 15:27:04 | 2019-07-27 15:25:00 | 3.62 | 2.985 | 2.930 | 2.910 | 2.942917 | 2.790 | 2.600 |
| 2019-07-30 15:34:20 | 2019-07-30 15:35:00 | 2.75 | 2.770 | 2.795 | 2.820 | 2.921591 | 2.820 | 2.820 |
zoo::rollapply.tsibbleGet the moving average value closet to flight time.
| collectDateTime | datetime | turb5min | ma01 | ma03 | ma04 | ma04u | ma06 | ma12 |
|---|---|---|---|---|---|---|---|---|
| 2020-06-24 17:36:27 | 2020-06-24 17:35:00 | 1.91 | 1.865 | 1.820 | 1.865 | 3.284375 | 1.845 | 1.9 |
| 2020-06-24 16:26:15 | 2020-06-24 16:25:00 | 1.51 | 1.795 | 1.875 | 1.840 | 2.687708 | 1.875 | 1.9 |
| 2020-06-24 16:18:44 | 2020-06-24 16:20:00 | 1.93 | 1.795 | 1.875 | 1.815 | 2.677292 | 1.875 | 1.9 |
| 2020-06-26 19:28:06 | 2020-06-26 19:30:00 | NA | NA | NA | NA | NaN | NA | NA |
| 2020-07-02 17:14:10 | 2020-07-02 17:15:00 | NA | NA | NA | NA | NaN | NA | NA |
use swchem_df date and times with the aop/ais matching function to determine which sampling dates have sensor data from same day around time of sampling.
Get fDOM sensor data from all AOS sampling dates (use ais with buffer2 function?)
show what is available
calculate rolling averages of fDOM time series
get rolling average values for time closest to sampling using which min on difftime
little overlap because of seasonal removal of buoys
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2014-05-14 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2014-06-19 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-10-19 15:30:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-11-16 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-12-19 17:53:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-01-18 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-02-13 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-03-22 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-04-12 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-05-03 14:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-08 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-07-06 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-08-03 14:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-09-06 14:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-10-19 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-11-01 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-01-04 17:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-02-13 16:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-03-13 16:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-04-17 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-05-01 16:26:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-06-05 15:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-30 16:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-12-04 16:42:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-01-10 17:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-02-11 17:55:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-03-26 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-05-07 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-12-04 17:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| collect_date | TSS | TDS |
|---|---|---|
| 2018-07-05 | 4 | 945.20 |
| 2018-08-07 | 6 | 935.64 |
| 2018-09-04 | 10 | 977.41 |
| 2018-10-16 | 34 | 1053.00 |
| 2019-06-04 | 8 | 872.15 |
| 2019-07-01 | 6 | 921.41 |
| 2019-07-01 | 8 | 915.63 |
| 2019-07-01 | 6 | 918.42 |
| 2019-08-06 | 10 | 744.60 |
| 2019-09-04 | 20 | 950.23 |
| 2019-09-04 | 18 | 942.21 |
| 2019-09-04 | 20 | 958.42 |
| 2019-10-16 | 12 | 833.27 |
Same plot as above with only QF = 0 data
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2014-05-14 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2014-06-19 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-10-19 15:30:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-11-16 16:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2016-12-19 17:53:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-01-18 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-02-13 16:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-03-22 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-04-12 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-05-03 14:20:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-06-08 14:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-07-06 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-08-03 14:50:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-09-06 14:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-10-19 15:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2017-11-01 15:15:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-01-04 17:00:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-02-13 16:45:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-03-13 16:05:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-04-17 16:03:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-05-01 16:26:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-06-05 15:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-30 16:07:00 | FALSE | FALSE | FALSE | FALSE |
| 2018-12-04 16:42:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-01-10 17:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-02-11 17:55:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-03-26 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-05-07 16:10:00 | FALSE | FALSE | FALSE | FALSE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-12-04 17:40:00 | FALSE | FALSE | FALSE | FALSE |
| 2021-05-04 15:22:00 | FALSE | FALSE | FALSE | FALSE |
| aos_datetime | check_3day | check_1day | check_6hr | check_1hr |
|---|---|---|---|---|
| 2018-07-05 15:20:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-08-07 16:16:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-09-04 15:50:00 | TRUE | TRUE | TRUE | TRUE |
| 2018-10-16 16:03:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-06-04 15:25:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-07-01 14:53:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-08-06 15:55:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-09-04 16:00:00 | TRUE | TRUE | TRUE | TRUE |
| 2019-10-16 17:05:00 | TRUE | TRUE | TRUE | TRUE |
## Warning: Removed 1279 rows containing missing values (geom_point).
Compare AOS and AIS
Colors indicate month